229 lines
8.9 KiB
C++
229 lines
8.9 KiB
C++
// ____ ______ __
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// / __ \ / ____// /
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// / /_/ // / / /
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// / ____// /___ / /___ PixInsight Class Library
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// /_/ \____//_____/ PCL 2.4.23
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// ----------------------------------------------------------------------------
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// pcl/MultiscaleLinearTransform.h - Released 2022-03-12T18:59:29Z
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// ----------------------------------------------------------------------------
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// This file is part of the PixInsight Class Library (PCL).
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// PCL is a multiplatform C++ framework for development of PixInsight modules.
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//
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// Copyright (c) 2003-2022 Pleiades Astrophoto S.L. All Rights Reserved.
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//
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// Redistribution and use in both source and binary forms, with or without
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// modification, is permitted provided that the following conditions are met:
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//
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// 1. All redistributions of source code must retain the above copyright
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// notice, this list of conditions and the following disclaimer.
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//
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// 2. All redistributions in binary form must reproduce the above copyright
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// notice, this list of conditions and the following disclaimer in the
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// documentation and/or other materials provided with the distribution.
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//
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// 3. Neither the names "PixInsight" and "Pleiades Astrophoto", nor the names
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// of their contributors, may be used to endorse or promote products derived
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// from this software without specific prior written permission. For written
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// permission, please contact info@pixinsight.com.
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//
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// 4. All products derived from this software, in any form whatsoever, must
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// reproduce the following acknowledgment in the end-user documentation
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// and/or other materials provided with the product:
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//
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// "This product is based on software from the PixInsight project, developed
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// by Pleiades Astrophoto and its contributors (https://pixinsight.com/)."
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//
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// Alternatively, if that is where third-party acknowledgments normally
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// appear, this acknowledgment must be reproduced in the product itself.
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//
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// THIS SOFTWARE IS PROVIDED BY PLEIADES ASTROPHOTO AND ITS CONTRIBUTORS
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// "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED
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// TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR
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// PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL PLEIADES ASTROPHOTO OR ITS
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// CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL,
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// EXEMPLARY OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, BUSINESS
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// INTERRUPTION; PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; AND LOSS OF USE,
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// DATA OR PROFITS) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
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// CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
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// ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
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// POSSIBILITY OF SUCH DAMAGE.
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// ----------------------------------------------------------------------------
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#ifndef __PCL_MultiscaleLinearTransform_h
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#define __PCL_MultiscaleLinearTransform_h
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/// \file pcl/MultiscaleLinearTransform.h
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#include <pcl/Defs.h>
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#include <pcl/Diagnostics.h>
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#include <pcl/RedundantMultiscaleTransform.h>
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namespace pcl
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{
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// ----------------------------------------------------------------------------
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/*!
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* \class MultiscaleLinearTransform
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* \brief A redundant multiscale transform using separable convolutions.
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*
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* The multiscale linear transform algorithm produces a set {w1,w2,...,wN,cN},
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* where each wj is a set of coefficients at scale j, which we call <em>detail
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* layer</em>, and cN is a large-scale smoothed residual, which we call
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* <em>residual layer</em>. Each layer has the same dimensions as the input
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* image, hence the generated multiscale transform is redundant.
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*
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* The algorithm applies successive convolutions with separable filter kernels
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* of increasing size 2*s + 1, where s grows following a monotonically
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* increasing sequence (the dyadic sequence 1, 2, 4, ... is used by default).
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* Multiscale coefficients are the differences between each pair of successive
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* convolved images. By default Gaussian filters are used, but block average
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* filters can also be used (see the class constructor) for special
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* applications.
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*
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* The reconstruction algorithm consists of the sum of all wj multiscale layers
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* for 1 <= j <= N, plus the residual layer cN.
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*
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* In our implementation, each layer in a multiscale linear transform is a
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* floating-point image with the same dimensions as the transformed image.
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* Layers are indexed from 0 to N. Layers at indexes from 0 to N-1 are detail
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* layers, whose elements are actually convolved difference coefficients.
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* Pixels in a detail layer can be negative, zero or positive real values.
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*
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* The last layer, at index N, is the large-scale <em>residual layer</em>.
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* Pixels in the residual layer image can only be positive or zero real values.
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*
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* \ingroup multiscale_transforms
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*/
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class PCL_CLASS MultiscaleLinearTransform : public RedundantMultiscaleTransform
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{
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public:
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/*!
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* Represents a multiscale transform layer.
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*/
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typedef RedundantMultiscaleTransform::layer layer;
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/*!
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* Represents a set of multiscale transform layers, or multiscale transform.
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*/
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typedef RedundantMultiscaleTransform::transform transform;
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/*!
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* Represents a set of layer enabled/disabled states.
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*/
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typedef RedundantMultiscaleTransform::layer_state_set layer_state_set;
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/*!
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* Constructs a %MultiscaleLinearTransform instance.
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*
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* \param n Number of detail layers. The transform will consist of \a n
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* detail layers plus a residual layer, that is n+1 total
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* layers. The default value is 4.
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*
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* \param d Scaling sequence. If \a d <= 0, the transform will use the
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* dyadic sequence: 1, 2, 4, ... 2^i. If \a d > 0, its value is
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* the distance in pixels between two successive scales.
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*
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* \param useMeanFilters If true, the transformation will use block
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* average filters (mean) instead of Gaussian filters. Mean
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* filters have important special applications, such as
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* computation of multiscale local variances. Gaussian filters
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* are always used by default.
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*
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* The default values for \a n and \a d are 4 and 0, respectively (four
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* layers and the dyadic scaling sequence).
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*
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* Successive layers are computed by applying separable convolutions with
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* kernel filters of size 2*s + 1. The scaling sequence parameter \a d
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* is interpreted as follows:
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*
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* - If the specified sequence parameter \a d is zero 0, then the transform
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* uses the dyadic sequence: s = 1, 2, 4, ..., 2^j for 0 <= j < n.
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*
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* - If \a d > 0, then \a d is the constant increment in pixels between two
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* successive scales (linear scaling sequence): s = d*j for 1 <= j < n.
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*/
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MultiscaleLinearTransform( int n = 4, int d = 0, bool useMeanFilters = false )
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: RedundantMultiscaleTransform( n, d )
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, m_useMeanFilters( useMeanFilters )
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{
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}
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/*!
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* Copy constructor.
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*/
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MultiscaleLinearTransform( const MultiscaleLinearTransform& ) = default;
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/*!
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* Move constructor.
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*/
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MultiscaleLinearTransform( MultiscaleLinearTransform&& ) = default;
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/*!
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* Destroys this %MultiscaleLinearTransform object. All existing transform
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* layers are destroyed and deallocated.
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*/
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virtual ~MultiscaleLinearTransform()
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{
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}
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/*!
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* Copy assignment operator. Returns a reference to this object.
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*/
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MultiscaleLinearTransform& operator =( const MultiscaleLinearTransform& ) = default;
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/*!
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* Move assignment operator. Returns a reference to this object.
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*/
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MultiscaleLinearTransform& operator =( MultiscaleLinearTransform&& ) = default;
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/*!
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* Returns true iff this transform applies block average filters instead of
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* Gaussian filters. See the class constructor for more information.
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*/
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bool UsesMeanFilters() const
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{
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return m_useMeanFilters;
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}
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/*!
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* Returns true iff this transform applies Gaussian filters instead of block
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* average filters. See the class constructor for more information.
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*/
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bool UsesGaussianFilters() const
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{
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return !m_useMeanFilters;
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}
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protected:
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/*
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* Whether we should use mean (block average) or Gaussian separable filters.
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*/
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bool m_useMeanFilters = false;
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/*
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* Transform (decomposition)
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*/
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void Transform( const pcl::Image& ) override;
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void Transform( const pcl::DImage& ) override;
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void Transform( const pcl::ComplexImage& ) override;
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void Transform( const pcl::DComplexImage& ) override;
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void Transform( const pcl::UInt8Image& ) override;
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void Transform( const pcl::UInt16Image& ) override;
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void Transform( const pcl::UInt32Image& ) override;
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friend class MLTDecomposition;
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};
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// ----------------------------------------------------------------------------
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} // pcl
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#endif // __PCL_MultiscaleLinearTransform_h
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// ----------------------------------------------------------------------------
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// EOF pcl/MultiscaleLinearTransform.h - Released 2022-03-12T18:59:29Z
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