3/10/2023 0 Comments Photomatix pro 4.2.5 download![]() The fused HDR image should have high bit depth, high contrast ratio, and preserved details. Our main contributions can be summarized as follows: To achieve that, we propose a motion-attention deep fusion network for HDR deghosting (DDFNet), which uses correlation and motion information to guide the merging of the LDR images and fuses HDR images without ghosting. Moreover, we need to avoid image distortion caused by motion estimation errors. The network should be able to fuse regions where saturation occurs and ignore texture differences caused by movement. Ideally, we expect a network to detect regions of differences between LDR images and determine whether these differences are the results of motion or saturation. In addition, correlation attention cannot distinguish the different areas caused by over-/under-exposure and object motion, resulting in ghosting effects on the motion area. However, correlation-guided feature alignment is sensitive to over-saturated regions, often losing textual details. Some of them introduce image correlation to guide the feature alignment, where the attention mechanism is applied to exclude misaligned features. ![]() To reduce the influence of misalignment of the LDR images, other DL-based methods, by contrast, use direct feature concatenation to fuse the LDR images. Due to inaccuracy in motion estimation, the aligned images will be distorted, resulting in artifacts in the HDR image fused by the neural networks. Hence, the fusion results are strongly influenced by the alignment algorithms. Many DL-based methods often pre-align LDR images before fusing them with neural networks to deal with motion problems. Recent work has shown that deep-learning-based methods often achieve state-of-the-art performance on various benchmark datasets. However, the introduced intensity thresholds usually results in inaccurate motion estimation. Methods such as attempt to detect moving objects by analyzing the consistency of pixel intensities between the input images. present iterative optimization frameworks based on low-rank minimization for obtaining fused images, which suffers from a high computational requirement. use patch-based motion estimation to align the input images, and their performance is heavily affected by the accuracy of the motion estimation. With regards to dynamic scenes, many MEF methods have been proposed, which can be broadly classified into conventional methods and deep-learning-based (DL-based) methods. However, moving objects are unavoidable when taking LDR images, resulting in ghosting effects after applying multi-exposure image fusion (MEF). In traditional multi-exposure fusion methods for static scenes, all low dynamic range (LDR) images are assumed to be perfectly aligned. Multi-exposure image fusion can provide high-quality HDR images at a low cost and is therefore widely used in the consumer electronics field. ![]() Exposure bracketing techniques, such as multi-exposure image fusion, address this computationally by capturing multiple images of the same scene at different exposure levels and then fusing them. However, they are prohibitively expensive and difficult to use for the average consumer. Professional HDR cameras can obtain HDR images. The limited capabilities of ordinary digital camera sensors make it challenging to reproduce scenes accurately with high dynamic ranges (HDR). Experimental results indicate that the proposed DDFNet achieves state-of-the-art image fusion performance on different public datasets. Following the merging of features, a decoder composed of Dense Networks reconstructs the HDR image without ghosting. The optical flow and correlation features are employed to adaptably combine information from LDR inputs in an attention-based fusion module. Additionally, it extracts correlation features between the reference LDR and other non-reference LDR images. Specifically, the DDFNet estimates the optical flow of the LDR images by a motion estimation module and encodes that optical flow as a flow feature. Taking advantage of both the alignment and attention-based methods, we propose an efficient Deep HDR Deghosting Fusion Network (DDFNet) guided by optical flow and image correlation attentions. Nevertheless, they also exclude the saturated details at the same time. In place of pre-alignment, attention-based methods calculate the correlation between the reference LDR image and non-reference LDR images, thus excluding misaligned regions in LDR images. The state-of-the-art methods use optical flow to align low dynamic range (LDR) images before merging, introducing distortion into the aligned LDR images from inaccurate motion estimation due to large motion and occlusion. Multi-exposure image fusion (MEF) methods for high dynamic range (HDR) imaging suffer from ghosting artifacts when dealing with moving objects in dynamic scenes.
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