Wide & deep learning for spatial & intensity adaptive image restoration

Most existing deep learning-based image restoration methods usually aim to remove degradation with uniform spatial distribution and constant intensity, making insufficient use of degradation prior knowledge. Here we bootstrap the deep neural networks to suppress complex image degradation whose intensity is spatially variable, through utilizing prior knowledge from degraded images. Specifically, we propose an ingenious and efficient multi-frame image restoration network (DparNet) with wide & deep architecture, which integrates degraded images and prior knowledge of degradation to reconstruct images with ideal clarity and stability. The degradation prior is directly learned from degraded images in form of key degradation parameter matrix, with no requirement of any off-site knowledge. The wide & deep architecture in DparNet enables the learned parameters to directly modulate the final restoring results, boosting spatial & intensity adaptive image restoration. We demonstrate the proposed method on two representative image restoration applications: image denoising and suppression of atmospheric turbulence effects in images. Two large datasets, containing 109,536 and 49,744 images respectively, were constructed to support our experiments. The experimental results show that our DparNet significantly outperform SoTA methods in restoration performance and network efficiency. More importantly, by utilizing the learned degradation parameters via wide & deep learning, we can improve the PSNR of image restoration by 0.6~1.1 dB with less than 2% increasing in model parameter numbers and computational complexity. Our work suggests that degraded images may hide key information of the degradation process, which can be utilized to boost spatial & intensity adaptive image restoration

SAMIHS: Adaptation of Segment Anything Model for Intracranial Hemorrhage Segmentation

Segment Anything Model (SAM), a vision foundation model trained on large-scale annotations, has recently continued raising awareness within medical image segmentation. Despite the impressive capabilities of SAM on natural scenes, it struggles with performance decline when confronted with medical images, especially those involving blurry boundaries and highly irregular regions of low contrast. In this paper, a SAM-based parameter-efficient fine-tuning method, called SAMIHS, is proposed for intracranial hemorrhage segmentation, which is a crucial and challenging step in stroke diagnosis and surgical planning. Distinguished from previous SAM and SAM-based methods, SAMIHS incorporates parameter-refactoring adapters into SAM's image encoder and considers the efficient and flexible utilization of adapters' parameters. Additionally, we employ a combo loss that combines binary cross-entropy loss and boundary-sensitive loss to enhance SAMIHS's ability to recognize the boundary regions. Our experimental results on two public datasets demonstrate the effectiveness of our proposed method. Code is available at https://github.com/mileswyn/SAMIHS .Comment: 5 pages, 3 figures, 2 table

Continuous photon energy modulation in IMRT of pancreatic cancer

Purpose: To develop a novel IMRT optimization method based on the principle of photon energy synthesis that simultaneously optimizes fluence map and beamlet energy. The method was validated on pancreatic cancers to demonstrate the benefits of the additional degree of freedom of photon energy in IMRT.Methods: Previous work has demonstrated that the effect of a photon beam of known energy can be achieved by the combination of two existing energy photons in the proper ratio. It further implied that any energy photon can be synthesized. Based on this, we propose the concept of continuous beamlet energy modulation in IMRT, or IMRT-BEM. The IMRT-BEM was modeled as the simultaneous optimization of two fluence maps, one for the low energy beam and one for the high energy beam, and it was implemented in an in-house inverse planning system. The IMRT-BEM was applied on 10 pancreatic cancer cases, where the IMRT-BEM plan was compared with single-energy IMRT plans of 6 MV (IMRT-6MV) and 15 MV photons (IMRT-15MV).Results: The IMRT-BEM plan provides a noticeable reduction to the volume irradiated at the high dose level (PTV105%) for PTV, at least 24.7% (6.4 ± 6.8 vs. 31.1 ± 18.7 (p = 0.005) and 43.8 ± 19.8 (p = 0.005) for IMRT-BEM, IMRT-6MV, and IMRT-15MV respectively). For target dose coverage, there were statistically significant improvements between the IMRT-BEM plans and the other two plans in terms of CI and HI. Compared to the IMRT-6MV plan, there were significant reductions in the Dmean of the spinal cord, liver, bowel, duodenum, and stomach. The irradiation volumes of the medium dose (V20Gy, and V40Gy) for the duodenum and bowel were reduced significantly. There were no significant differences between the IMRT-BEM and IMRT-15MV plans except for the Dmean of the spinal cord and the duodenum, the V20Gy, and V40Gy for the duodenum, and the V20Gy of the stomach.Conclusion: IMRT-BEM has certain dosimetric advantages for PTV and improves OAR sparing in pancreatic cancer, and can be effectively used in radiation treatment planning, providing another degree of freedom for planners to improve treatment plan quality

Survey on Dim Small Target Detection in Clutter Background: Wavelet, Inter-Frame and Filter Based Algorithms

AbstractDim small target is an active and important research area in image processing and pattern recognition. Various algorithms have been proposed to detect and track dim small target. This paper reviews some algorithms for dim small target detection, including the wavelet based algorithms, inter-frame difference based algorithms and filter based algorithms. Also, the further development of the technologies has been briefly analyzed

Infrared and Visual Image Fusion through Fuzzy Measure and Alternating Operators

The crucial problem of infrared and visual image fusion is how to effectively extract the image features, including the image regions and details and combine these features into the final fusion result to produce a clear fused image. To obtain an effective fusion result with clear image details, an algorithm for infrared and visual image fusion through the fuzzy measure and alternating operators is proposed in this paper. Firstly, the alternating operators constructed using the opening and closing based toggle operator are analyzed. Secondly, two types of the constructed alternating operators are used to extract the multi-scale features of the original infrared and visual images for fusion. Thirdly, the extracted multi-scale features are combined through the fuzzy measure-based weight strategy to form the final fusion features. Finally, the final fusion features are incorporated with the original infrared and visual images using the contrast enlargement strategy. All the experimental results indicate that the proposed algorithm is effective for infrared and visual image fusion

Simulation and Analysis of Influencing Factors of Solar Energy Inter-seasonal Soil Heat Storage

Taking an office building in Jinan as an example, the simulation model of solar inter-seasonal soil heat storage was established by TRNSYS software, and the variation law of ground temperature in the heat storage period was analyzed. From the perspective of ground temperature change, the influence of the spacing, length, number of drilling wells and area of solar collector on the heat storage effect was analyzed. The results showed that the soil temperature increased rapidly at the beginning of heat storage, and then the temperature rise rate gradually slowed down. The ground heat exchanger spacing, length, number of drilling and collector area will have a great influence on the solar energy seasonal heat storage effect. Therefore, in practical engineering applications, for the solar inter-seasonal soil heat storage system, the parameters of buried pipes, collectors and other components are recommended to be reasonably determined by simulation to obtain the optimal heat storage effect