EP-4571671-B1 - FPGA-BASED NLMEANS NOISE-REDUCTION SIMPLIFICATION METHOD AND SYSTEM FOR INFRARED IMAGE, AND MEDIUM AND DEVICE

Inventors

• LUO, YAN
• LUO, BING
• LI, JIANGHUI
• Chen, Chengzhi
• ZHANG, LEI

Dates

Publication Date

20260506

Application Date

20231218

Claims (8)

1. A simplified computer-implemented method for denoising an infrared image by using non-local means (NLMeans) based on a field programmable gate array (FPGA), characterized by comprising: step S1: determining a size of a search window and a size of a matching window; step S2: performing linear superposition on a Manhattan distance and a Chebyshev distance to calculate a similarity between each matching block and a central block; and step S3: calculating a weight of each matching block based on the similarity, and calculating a current denoising result based on each matching block and the corresponding weight.
2. The simplified method for denoising the infrared image by using NLMeans based on the FPGA according to claim 1, characterized in that the step S1 comprises: determining the size of the search window as N*N and the size of the matching window as d*d, wherein when N=11 and d=3, there are a total of 81 matching blocks.
3. The simplified method for denoising the infrared image by using NLMeans based on the FPGA according to claim 1, characterized in that in the step S2, a following formula is adopted: S i = 1 − a ∑ a i − b i + a max a i − b i , wherein S i represents the similarity; a represents a parameter, and each of a i and b i represents a pixel value of an image block; and a subscript i represents a same position in two image blocks.
4. A simplified system for denoising an infrared image by using NLMeans based on an FPGA, characterized by comprising: a module M1 configured to determine a size of a search window and a size of a matching window; a module M2 configured to perform linear superposition on a Manhattan distance and a Chebyshev distance to calculate a similarity between each matching block and a central block; and a module M3 configured to calculate a weight of each matching block based on the similarity, and calculate a current denoising result based on each matching block and the corresponding weight.
5. The simplified system for denoising the infrared image by using NLMeans based on the FPGA according to claim 4, characterized in that the module M1 determines the size of the search window as N*N and the size of the matching window as d*d; and when N=11 and d=3, there are a total of 81 matching blocks.
6. The simplified system for denoising the infrared image by using NLMeans based on the FPGA according to claim 4, characterized in that the module M2 adopts a following formula: S i = 1 − a ∑ a i − b i + a max a i − b i , wherein S i represents the similarity; a represents a parameter, and each of a i and b i represents a pixel value of an image block; and a subscript i represents a same position in two image blocks.
7. A computer-readable storage medium storing a computer program, characterized in that the computer program is executed by a processor to perform steps of the method according to any one of claims 1 to 3.
8. An electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable in the processor, characterized in that the computer program is executed by the processor to perform steps of the simplified method for denoising the infrared image by using NLMeans based on the FPGA according to any one of claims 1 to 3.

Description

TECHNICAL FIELD The present disclosure relates to the technical field of denoising an infrared image, and specifically, to a simplified method and system for denoising an infrared image by using non-local means (NLMeans) based on a field programmable gate array (FPGA), a medium, and a device. BACKGROUND At present, infrared imaging technology has been widely applied in military, industrial, agricultural, and other fields. Increasing application demands have put forward a higher requirement for quality of an infrared image. However, affected by factors such as a detector material, processing method, and external environment, the infrared image often contains some noise, which not only reduces the quality of the infrared image but also affects extraction of effective information in the infrared image. Numerous studies have been conducted on denoising of the infrared image in the industry, and NLMeans is one of most effective denoising algorithms. Many infrared devices are portable devices. Therefore, many infrared devices are based on an FPGA platform to implement an algorithm, which can well meet a usage demand of a real-time scene. However, an FPGA has a certain resource limitation and cannot perform particularly complex floating-point process calculation. Therefore, some algorithms have complex calculation processes and are inconveniently implemented on the FPGA. The patent document CN107590783A (application No. 201710754287.0) discloses an FPGA-based image denoising method, including: inputting image data into a template generation module, processing row data of a video image by using a 1x7 neighborhood template, and calculating the corresponding noise variance based on an amplitude of input noise; performing, by using a median filtering module, median filtering on seven rows of generated original image data and the noise variance, calculating medians of original video image data under four types of strides as well as the respective corresponding noise variance medians, and outputting a result to a data analysis and selection module; calculating a range of an image signal for each stride based on video image data and noise variances corresponding to different strides, and selecting an optimal stride based on the range; and outputting data through a data output module. In response to the above problems, based on a special structure of the FPGA and a traditional NLMeans denoising algorithm, the present disclosure provides a simplified algorithm for denoising the infrared image by using the NLMeans based on the FPGA. This algorithm can effectively remove noise from an original infrared image and can be easily implemented on the FPGA. SUMMARY In order to overcome the defects in the prior art, the present disclosure is intended to provide a simplified method and system for denoising an infrared image by using NLMeans based on an FPGA, a medium, and a device. A simplified method for denoising an infrared image by using NLMeans based on an FPGA is provided according to the present disclosure, including: step S1: determining a size of a search window and a size of a matching window;step S2: performing linear superposition on a Manhattan distance and a Chebyshev distance to calculate a similarity between each matching block and a central block; andstep S3: calculating a weight of each matching block based on the similarity, and calculating a current denoising result based on each matching block and the corresponding weight. Preferably, the step S1 includes: determining the size of the search window as N*N and the size of the matching window as d*d, where when N=11 and d=3, there are a total of 81 matching blocks. Preferably, in the step S2, a following formula is adopted: Si=1−a∑ai−bi+amaxai−bi, where Si represents the similarity; a represents a parameter, and each of ai and bi represents a pixel value of an image block; and the subscript i represents a same position in two image blocks, where i = 1,2...,9 . A simplified system for denoising an infrared image by using NLMeans based on an FPGA is provided according to the present disclosure, including: a module M1 configured to determine a size of a search window and a size of a matching window;a module M2 configured to perform linear superposition on a Manhattan distance and a Chebyshev distance to calculate a similarity between each matching block and a central block; anda module M3 configured to calculate a weight of each matching block based on the similarity, and calculate a current denoising result based on each matching block and the corresponding weight. Preferably, the module M1 determines the size of the search window as N*N and the size of the matching window as d*d; and when N=11 and d=3, there are a total of 81 matching blocks. Preferably, the module M2 adopts a following formula: Si=1−a∑ai−bi+amaxai−bi, where Si represents the similarity; a represents a parameter, and each of ai and bi represents a pixel value of an image block; and the subscript i re