Aug 12, 2023 · Convolutional layers apply a set of learnable filters to the input image, capturing different features and patterns. Each filter detects specific visual features, such as edges, textures, and...

Feb 27, 2025 · The DenseNet encoder extracts feature information from the formula image. The MSR module addresses the loss of detailed features caused by downsampling operations in the encoder, using a hierarchical residual structure to better preserve fine-grained details.

Apr 1, 2022 · In this paper, we propose a novel method for visible and infrared image fusion by decomposing feature information, which is termed as CUFD. It adopts two pairs of encoder–decoder networks to implement feature map extraction and decomposition, respectively.

Feb 28, 2025 · Through the strategy of image partitioning, the accuracy of semantic segmentation has been enhanced. The "Dual Encoder – Quad Decoder" architecture with partitioning enhances semantic transmission. The Bottleneck module, which progressively fuses semantics, enhances semantic extraction.

In the encoder stage, the input image first undergoes feature extraction via Res2Net blocks using 3×3 convolutional kernels. Following each convolution, a maximum-pooling layer is applied to extract prominent boundary and texture features from the feature maps.

Feb 10, 2025 · Image denoising is crucial for correcting distortions caused by environmental factors and technical limitations. We propose a novel and highly robust encoder–decoder network (HREDN) for effectively removing mixed salt-and-pepper and Gaussian noise from digital images.

2 days ago · Recognizing that the universal capabilities of retrieval systems depend on the image representation, this study delves into the realm of universal feature extraction. Therefore, the primary objective was to efficiently develop and train a universal image encoder capable of extracting discriminative image features specifically tailored for image ...

3 days ago · Dual-stream encoder architecture: We propose a novel encoder that leverages both even and odd kernel convolutions for enhanced multi-scale feature extraction. Even kernels capture continuous ...

6 days ago · Although deep learning has significantly advanced brain tumor MRI segmentation and preoperative planning, existing methods like U-Net and Transformer, which are widely used Encoder–Decoder architectures in medical image segmentation, still have limitations. Specifically, these methods fail to fully leverage the unique characteristics of different MRI modalities during the feature extraction ...

3 days ago · The DDT introduces a condition encoder and a velocity decoder to handle low- and high-frequency components in image generation separately. The encoder extracts semantic features (zt) from noisy inputs, timesteps, and class labels, which are then used by the decoder to estimate the velocity field. To ensure consistency of zt across steps ...