Deconvolution Optimization Bypassing Redundant Dot Products
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Solution Overview
Problem
Deconvolution processes, particularly in image upscaling, are computationally exhaustive due to the numerous dot product operations required for generating and convolving intermediate images with kernels, leading to inefficiencies in computational resources and power consumption.
Innovation Solution
The method involves performing a first dot product on a subset of the input image pixels and a portion of the kernel to generate an output image pixel, while bypassing dot product operations on specific portions of the kernel based on mapping information, thereby reducing the number of multiplications and avoiding the generation of intermediate images, thus optimizing computational efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional deconvolution is performed by generating intermediate image and convolving with kernel, then image upscaling is achieved, but computational complexity and processing time increase significantly
Solution Approach 1:
The patent extracts and identifies redundant dot product operations in the deconvolution process. By analyzing the mathematical formulation of deconvolution, the invention determines which dot products can be bypassed without affecting the final output quality, thereby eliminating unnecessary computational steps while maintaining image upscaling accuracy.
Solution Approach 2:
The patent applies partial action by performing only the necessary dot product operations required for accurate deconvolution. Instead of computing all possible dot products between intermediate image pixels and kernel elements, the invention selectively performs only those dot products that contribute to the final output, achieving the same result with fewer computations.
2Reliability
If traditional deconvolution performs all dot product operations on intermediate image and kernel, then complete convolution is achieved, but computational resources are wasted
Solution Approach 1:
The patent extracts redundant computational operations from the traditional deconvolution process. By identifying which dot product operations do not contribute to the final output (particularly those involving padded regions), the invention removes these unnecessary computations, thereby reducing energy consumption while preserving convolution accuracy for the essential operations.
Solution Approach 2:
The patent applies local quality by treating different regions of the computation differently. Regions corresponding to actual image data are processed with full convolution accuracy, while regions corresponding to padded areas are bypassed. This selective processing maintains reliability where needed while conserving computational energy in redundant regions.
3Adaptability or versatility
If intermediate image is generated by padding pixels before convolution, then deconvolution can be performed, but number of computations increases
Solution Approach 1:
The patent extracts and eliminates the computationally expensive intermediate image generation and padding steps. By directly identifying which original image pixels correspond to which output pixels through mathematical analysis, the invention bypasses the need to create and process the intermediate padded image entirely, thereby maintaining deconvolution capability while dramatically improving processing efficiency.
Solution Approach 2:
The patent performs preliminary analysis of the deconvolution mathematics to determine the exact mapping between input and output pixels before execution. By pre-calculating which dot product operations are necessary and which can be bypassed, the invention eliminates redundant intermediate steps and directly performs only the essential computations, improving productivity without sacrificing adaptability.
Data Source
AI summary
Disclosed herein includes a system, a method, and a device for improving computational efficiency of deconvolution by reducing a number of dot products. In one aspect, an input image having a set of pixels is received. A first dot product may be performed on a subset of the set of pixels of the input image and a portion of a kernel, to generate a first pixel of an output image. A number of multiplications performed for the first dot product performed may be less than a number of elements of the kernel. A second dot product on a remaining portion of the kernel to generate the first pixel of the output image may be bypassed.