Converting a camera’s RAW image to an RGB format for human perception involves utilizing an imaging pipeline, and a series of processing modules. Existing modules often result in varying degrees of original information loss, which can render the reverse imaging pipeline unable to recover the original RAW image information. To this end, this paper proposes a new, almost reversible image imaging pipeline. Thus, RGB images and RAW images can be effectively converted between each other. Considering the impact of original information loss, this paper introduces a two-stage tone mapping operation (TMO). In the first stage, the RAW image with a linear response is transformed into an RGB color image. In the second stage, color scale mapping corrects the dynamic range of the image suitable for human perception through linear stretching, and reduces the loss of sensitive information to the human eye during the integer process. effectively preserving the original image’s dynamic information. The DCRAW imaging pipeline addresses the problem of high light overflow by directly highlighting cuts. The proposed imaging pipeline constructs an independent highlight processing module, and preserves the highlighted information of the image. The experimental results demonstrate that the two-stage tone mapping operation embedded in the imaging processing pipeline provided in this article ensures that the image output is suitable for human visual system (HVS) perception and retains more original image information.

Accurate semantic segmentation of remote sensing (RS) imagery faces significant challenges due to the presence of small targets, ambiguous features, and complex scenes. Conventional convolutional networks, although computationally efficient, do not adequately perceive the global context. In contrast, transformers excel in global context awareness but incur a higher computational cost. To bridge this gap, we introduce BotWaveNet, an innovative semantic segmentation framework designed for RS images. This framework is inspired by the global context proficiency exhibited by BoTNet. BotWaveNet reconfigures the fourth layer of ResNet by replacing conventional convolutional operations with a spatial attention mechanism (SAM), thus establishing an encoder–decoder structure that integrates attention mechanisms within deep network architectures. The SAM module is specifically engineered to enhance edge delineation by promoting pixel interconnectivity, thereby capturing global context more effectively. Furthermore, we have designed a feature fusion module (wavelet attention module) that fine-tunes the extraction of channel features, optimizing adaptability for both synthetic aperture radar and optical images. In an effort to reduce the computational demands associated with MaxPooling, we have incorporated a wavelet transform module. This module serves as an innovative replacement for traditional convolutional pooling methods, aiming to maintain efficiency while enhancing performance. The efficacy of BotWaveNet has been substantiated through its significant performance enhancements observed in the Gaofen Challenge and the Inria Aerial Image Labeling Dataset.