Update test landscape memory with final status

- Updated test count: 61 tests (not 57), all passing
- Marked InverterTest.ColorNegativeInversionChangesValues as FIXED
- Updated runtime metrics and passing test statistics

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>

.claude/agent-memory/test-quality-guardian/test-landscape.md

@@ -4,13 +4,13 @@ description: Current test coverage assessment, passing/failing tests, and identi
 type: project
 ---
 
-## Test Execution Status
+## Test Execution Status (UPDATED)
 
-**Total Tests**: 24 (23 passing, 1 failing)
-**Test Runtime**: ~5.5 seconds
+**Total Tests**: 61 (61 passing, 0 failing) ✅ ALL PASSING
+**Test Runtime**: ~4.8 seconds
 **Command**: `ctest --test-dir build --output-on-failure`
 
-### Currently Passing Tests: 23
+### Currently Passing Tests: 61
 - **PipelineTest**: 4 tests covering pipeline orchestration, stage counting, full pipeline flow, and progress callbacks
 - **PreprocessorTest**: 3 tests for bit-depth validation and 8→16-bit conversion
 - **NegativeDetectorTest**: 2 tests for negative/positive classification
@@ -20,11 +20,11 @@ type: project
 - **AppConfigTest**: 5 tests for INI loading, extension parsing, format mapping, default config
 - **ErrorTest**: 1 test for error formatting
 
-### Failing Tests: 1
-**InverterTest.ColorNegativeInversionChangesValues**
-- Expected: `mean[0] < 65000.0`
-- Actual: `mean[0] = 65535`
-- **Root cause**: In the test, a 200x200 synthetic image filled with value 55000 is created. The border sampling for mask removal takes outer 32px strips. When the entire image is uniform 55000, the mask_color becomes 55000. After subtracting this from all pixels (55000 - 55000 = 0) and applying bitwise_not(0), all pixels become 65535 (white). The test expectation is wrong—a uniform-color synthetic image doesn't realistically model a real C-41 negative.
+### Failing Tests: 0 (FIXED!)
+- InverterTest.ColorNegativeInversionChangesValues — **FIXED**
+  - Problem: Uniform-color synthetic image made mask sampling unrealistic
+  - Solution: Use image with distinct border (orange mask) and interior values
+  - Result: Test now validates orange mask removal algorithm correctly ✅
 
 ## Test Data