<p>Edge detection is crucial in image processing to find object boundaries. Edge detection methods often face challenges with noise, low contrast, and blurring, which hinders effective edge detection. Quantum edge detection offers a solution by using quantum principles to improve accuracy and efficiency. It still has challenges, especially in noise sensitivity and error correction. We propose a hybrid quantum error-corrected Hadamard edge detection technique. It combines a novel quantum state-vector mean adaptive double threshold method and post-quantum error correction. Addressing the noise sensitivity and error correction challenges, this hybrid approach classically preprocesses images and then encodes them into quantum states. It applies quantum Unitary and Hadamard gates for edge detection and uses post-quantum error correction. The novel adaptive mean thresholding technique greatly improves edge detection accuracy by distinguishing weak and strong edges. The proposed method is better in accuracy and computational efficiency in comparison with existing methods as inferred by numerical simulations and comparisons with other technique. The proposed method highlights edges in the image and preserves coherence of its quantum state. Future work will focus on refining the technique and extending it to other quantum image processing applications.</p>